Many Internet Service Providers (“ISPs”) provide Enhanced Internet Protocol (“EIP”) real time services such as voice-over IP, fax-over IP, unified messaging, and Internet call waiting over the IP network. The EIP real-time services differ from non-real time, best-effort services provided over Transmission Control Protocol (“TCP”); unlike best-effort services, EIP services have end-to-end quality of service (QOS) requirements for packet loss, delay and delay jitter. As a result, providing EIP services requires new processes for network resource planning.
Current processes are mostly based on a simple calculation of network link usage. However, this method of planning may result in poor quality for EIP services because it fails to address or estimate end-to-end QOS requirements, such packet loss, delay and delay jitter.
Although these problems have been addressed by prior art methods, many known methods make Poisson arrival and/or exponential packet length assumptions about service traffic characteristics. In most cases, these assumptions are not applicable for EIP service traffic because different types of services have different packet length and inter-arrival distributions. Even for the same service, e.g. Voice over IP (“VoIP”), different voice encoders generate different traffic statistics. When silence compression capability is de-activated in the encoders, the resulting voice traffic consists of packets with constant length and constant inter-arrival time. Once the silence compression capability is activated, packet inter-arrival times vary.
Given the mix of many types of deterministic and non-deterministic service traffic, approaches using large deviation theory to estimate end-to-end QOS have been suggested. However, large deviation theory-based estimates assume that there are a fixed number of active sources feeding the network. For example, large deviation theory calculations may assume that there are n1 voice calls, n2 fax calls, and n3 e-mail messaging calls, n1, n2, and n3 being fixed numbers, which are active and that generate traffic at any given time. In reality, the number of active sources feeding the network varies over time because calls are constantly connected and disconnected over any given time period, thereby increasing or decreasing, respectively, the total number of active calls. Thus, a direct application of the large deviation theory as taught by the known art is not the most appropriate method for estimating end-to-end QOS.
There is need for a process that estimates end-to-end packet loss, delay, and delay jitter for EIP network planning using the large deviation theory while taking into account changes in the number of active sources feeding the network.